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Application of Fuzzy Clustering Methodology for Garment Sizing

Received: 16 April 2019     Accepted: 28 May 2019     Published: 12 June 2019
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Abstract

With the growing demand for Ready-To-Wear outfits especially in African textile prints, the currently used European, American and Asian garment sizing systems seems unsuitable for the Nigerian garment industry where customer’s choose clothing item not only due to fit in terms of body measurements but also the dress culture, style, preference and some other implicit requirements. This study aims to develop a size chart for different styles of trousers worn by Nigeria male population. Anthropometric data of 500 customers were taken in a natural random process and from stable tailoring establishments. The data was analysed using descriptive statistics and the fuzzy clustering methodology (FCM) was used as a suggestive approach which describes subjectivity in customer preferences. Analysis of the FCM output shows that the number of individual measurements with misfit has no significant difference (Festimated= 1.119, p-value=0.375 and Fcritical= 2.866) across cluster. The percentages of misfit were 38.0, 23.4, 31.6, 31.4 and 3.8% for hip measurement, length, waist, thigh and bottom-girth respectively. The developed sizing system which reflects subjectivity in customer’s selection of trouser may also enhance both producer and retailer’s production and replenishment policy.

Published in American Journal of Data Mining and Knowledge Discovery (Volume 4, Issue 1)
DOI 10.11648/j.ajdmkd.20190401.15
Page(s) 24-31
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Ready-To-Wear, Size Chart, Trousers, Fuzzy Clustering

References
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[2] Otieno, A. D., Mehtre, A., Fera O, Lema O., O., Gebeyehu S. Developing Standard Size Charts for Ethiopian Men between the Ages of 18-26 through Anthropometric Survey. J Text Apparel, Technol Manag. 2016; 10 (1): 1–10.
[3] Hsu C, Lee T, Kuo H. Mining the Body Features to Develop Sizing Systems to Improve Business Logistics and Marketing Using Fuzzy Clustering Data Mining. WSEAS Trans Comput. 2009; 8 (7): 1215–24.
[4] Adelaja O, Salusso CJ. Designing apparel for Nigerian women : addressing visual appeal, body type and sizing. In: International Textile and Apparel Association Annual Conference Proceedings. 2015. p. 1–3.
[5] Salusso. A method for classifying adult female body form variation in relation to the US Standard for apparel sizing. Doctoral Dissertation, University of Minnesota; 1982.
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[17] M. Vishnu Vardhana Rao, Sharad Kumar and G. N. V. Brahmam. A study of the geographical clustering of districts in Uttar Pradesh using nutritional anthropometric data of preschool children. Indian J Med Res. 2013; 137 (1): 73–81.
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Cite This Article
  • APA Style

    Adepeju Abimbola Opaleye, Adekunle Kolawole, Oliver Ekepre Charles-Owaba. (2019). Application of Fuzzy Clustering Methodology for Garment Sizing. American Journal of Data Mining and Knowledge Discovery, 4(1), 24-31. https://doi.org/10.11648/j.ajdmkd.20190401.15

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    ACS Style

    Adepeju Abimbola Opaleye; Adekunle Kolawole; Oliver Ekepre Charles-Owaba. Application of Fuzzy Clustering Methodology for Garment Sizing. Am. J. Data Min. Knowl. Discov. 2019, 4(1), 24-31. doi: 10.11648/j.ajdmkd.20190401.15

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    AMA Style

    Adepeju Abimbola Opaleye, Adekunle Kolawole, Oliver Ekepre Charles-Owaba. Application of Fuzzy Clustering Methodology for Garment Sizing. Am J Data Min Knowl Discov. 2019;4(1):24-31. doi: 10.11648/j.ajdmkd.20190401.15

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  • @article{10.11648/j.ajdmkd.20190401.15,
      author = {Adepeju Abimbola Opaleye and Adekunle Kolawole and Oliver Ekepre Charles-Owaba},
      title = {Application of Fuzzy Clustering Methodology for Garment Sizing},
      journal = {American Journal of Data Mining and Knowledge Discovery},
      volume = {4},
      number = {1},
      pages = {24-31},
      doi = {10.11648/j.ajdmkd.20190401.15},
      url = {https://doi.org/10.11648/j.ajdmkd.20190401.15},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajdmkd.20190401.15},
      abstract = {With the growing demand for Ready-To-Wear outfits especially in African textile prints, the currently used European, American and Asian garment sizing systems seems unsuitable for the Nigerian garment industry where customer’s choose clothing item not only due to fit in terms of body measurements but also the dress culture, style, preference and some other implicit requirements. This study aims to develop a size chart for different styles of trousers worn by Nigeria male population. Anthropometric data of 500 customers were taken in a natural random process and from stable tailoring establishments. The data was analysed using descriptive statistics and the fuzzy clustering methodology (FCM) was used as a suggestive approach which describes subjectivity in customer preferences. Analysis of the FCM output shows that the number of individual measurements with misfit has no significant difference (Festimated= 1.119, p-value=0.375 and Fcritical= 2.866) across cluster. The percentages of misfit were 38.0, 23.4, 31.6, 31.4 and 3.8% for hip measurement, length, waist, thigh and bottom-girth respectively. The developed sizing system which reflects subjectivity in customer’s selection of trouser may also enhance both producer and retailer’s production and replenishment policy.},
     year = {2019}
    }
    

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  • TY  - JOUR
    T1  - Application of Fuzzy Clustering Methodology for Garment Sizing
    AU  - Adepeju Abimbola Opaleye
    AU  - Adekunle Kolawole
    AU  - Oliver Ekepre Charles-Owaba
    Y1  - 2019/06/12
    PY  - 2019
    N1  - https://doi.org/10.11648/j.ajdmkd.20190401.15
    DO  - 10.11648/j.ajdmkd.20190401.15
    T2  - American Journal of Data Mining and Knowledge Discovery
    JF  - American Journal of Data Mining and Knowledge Discovery
    JO  - American Journal of Data Mining and Knowledge Discovery
    SP  - 24
    EP  - 31
    PB  - Science Publishing Group
    SN  - 2578-7837
    UR  - https://doi.org/10.11648/j.ajdmkd.20190401.15
    AB  - With the growing demand for Ready-To-Wear outfits especially in African textile prints, the currently used European, American and Asian garment sizing systems seems unsuitable for the Nigerian garment industry where customer’s choose clothing item not only due to fit in terms of body measurements but also the dress culture, style, preference and some other implicit requirements. This study aims to develop a size chart for different styles of trousers worn by Nigeria male population. Anthropometric data of 500 customers were taken in a natural random process and from stable tailoring establishments. The data was analysed using descriptive statistics and the fuzzy clustering methodology (FCM) was used as a suggestive approach which describes subjectivity in customer preferences. Analysis of the FCM output shows that the number of individual measurements with misfit has no significant difference (Festimated= 1.119, p-value=0.375 and Fcritical= 2.866) across cluster. The percentages of misfit were 38.0, 23.4, 31.6, 31.4 and 3.8% for hip measurement, length, waist, thigh and bottom-girth respectively. The developed sizing system which reflects subjectivity in customer’s selection of trouser may also enhance both producer and retailer’s production and replenishment policy.
    VL  - 4
    IS  - 1
    ER  - 

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Author Information
  • Department of Industrial and Production Engineering, University of Ibadan, Ibadan, Nigeria

  • Department of Industrial and Production Engineering, University of Ibadan, Ibadan, Nigeria

  • Department of Industrial and Production Engineering, University of Ibadan, Ibadan, Nigeria

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